GSFormer: Geometric-Spatial Transformer on Point Cloud Completion

Point cloud completion aims to complete the objects’ shape from incomplete 3D objects. Most works based on encoder-decoder lose the local geometric details of global features when encoding partial points. Besides, the decoder lacks the exploration of the correlation between global and local features. To solve these problems, 1) we propose a Geometric Transformer to learn the global and local geometric details of incomplete point clouds by learning their shape prior geometric information in the encoder, which is beneficial to generate geometric keypoints. The generated geometric keypoints contain the global structure information and local geometric details of the complete point cloud. 2) We propose a Spatial Transformer in the decoder, which can adaptively select neighborhood features to learn the long-distance geometric relationship between upsampling points. Experimental results show that our method achieves better performance on PCN and Shapenet-55/34 datasets.